Knowledge representation for process diagnosis expert systems has evolved from simple rule-based systems, known as shallow knowledge, to more complex model-based systems, or deep knowledge. Shallow knowledge represents the domain information through a set of "if . . . then" rules. These rules are generally acquired from a domain expert based on experience and judgmental knowledge with no functional representation of the underlying phenomena. The weakness of rule-based systems is one of verification and validation. Procedures cannot be developed to test heuristically generated rules for correctness and completeness. Even if the diagnostic rules are generated in a systematic fashion, diagnostic event-based rules cannot guarantee functional completeness. It is simply not possible to anticipate and formulate rules to cover every conceivable system situation. Deep knowledge represents the domain information through mathematical models of the process under consideration. This model-based system in the form of quantitative and qualitative simulation algorithms describes the underlying phenomena of the physical system.
To alleviate the limitations of rule-based systems, attempts have been made to combine both shallow and deep knowledge as the knowledge structure of a process diagnostic expert system. One approach is to use shallow rules to hypothesize about the possible failures first, then follow with deep knowledge reasoning to test each one of the hypotheses. The success of this approach is highly dependent on the ability of the shallow rules, which cannot in general be verified and validated, to hypothesize correct faulty candidates.
The present invention addresses and overcomes the aforementioned limitations of the prior art by providing a method of diagnosing failures in the operation of a process by identifying faulty component candidates of process malfunctions through basic physical principles of conservation, functional classification of components, component characteristics and information from the process schematics. Except for the information from the process schematics, the method is completely general and independent of the process under consideration. In contrast to the prior art heuristic construction of a rigid knowledge base that uses an event-oriented approach for process diagnosis, the present invention employs the systematic construction of a hierarchical structure knowledge base with two levels, where the first level is function-based and the second level is component characteristic-based.